R All-in-One For Dummies

A deep dive into the programming language of choice for statistics and data

With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious. This book has your back. Along with providing an overview of coding in R and how to work with the language, this book delves into the types of projects and applications R programmers tend to tackle the most. You'll find coverage of statistical analysis, machine learning, and data management with R.

• Grasp the basics of the R programming language and write your first lines of code
• Understand how R programmers use code to analyze data and perform statistical analysis
• Use R to create data visualizations and machine learning programs 
• Work through sample projects to hone your R coding skill

This is an excellent all-in-one resource for beginning coders who'd like to move into the data space by knowing more about R.

Joseph Schmuller is a cognitive scientist and statistical analyst. His recent work in the For Dummies series includes the 5th edition of Statistical Analysis with Excel For Dummies along with Statistical Analysis with R For Dummies and R Projects For Dummies.

Introduction 1

Book 1: Introducing R 5

Chapter 1: R: What It Does and How It Does It 7

Chapter 2: Working with Packages, Importing, and Exporting 37

Book 2: Describing Data 51

Chapter 1: Getting Graphic 53

Chapter 2: Finding Your Center 93

Chapter 3: Deviating from the Average 103

Chapter 4: Meeting Standards and Standings 113

Chapter 5: Summarizing It All 125

Chapter 6: What’s Normal? 145

Book 3: Analyzing Data 163

Chapter 1: The Confidence Game: Estimation 165

Chapter 2: One-Sample Hypothesis Testing 181

Chapter 3: Two-Sample Hypothesis Testing 207

Chapter 4: Testing More than Two Samples 233

Chapter 5: More Complicated Testing 257

Chapter 6: Regression: Linear, Multiple, and the General Linear Model 279

Chapter 7: Correlation: The Rise and Fall of Relationships 315

Chapter 8: Curvilinear Regression: When Relationships Get Complicated 335

Chapter 9: In Due Time 359

Chapter 10: Non-Parametric Statistics 371

Chapter 11: Introducing Probability 393

Chapter 12: Probability Meets Regression: Logistic Regression 415

Book 4: Learning from Data 423

Chapter 1: Tools and Data for Machine Learning Projects 425

Chapter 2: Decisions, Decisions, Decisions 449

Chapter 3: Into the Forest, Randomly 467

Chapter 4: Support Your Local Vector 483

Chapter 5: K-Means Clustering 503

Chapter 6: Neural Networks 519

Chapter 7: Exploring Marketing 537

Chapter 8: From the City That Never Sleeps 557

Book 5: Harnessing R: Some Projects to Keep You Busy 573

Chapter 1: Working with a Browser 575

Chapter 2: Dashboards — How Dashing! 603

Index 639

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